给出python实现策略自适应和参数自适应的SADE算法的代码

以下是一个简单的Python实现，使用SADE算法实现策略自适应和参数自适应：

import random

class SADE:
    def __init__(self, pop_size, f, cr, dim, bounds):
        self.pop_size = pop_size
        self.f = f
        self.cr = cr
        self.dim = dim
        self.bounds = bounds

    def optimize(self, max_iter):
        # 初始化种群
        pop = [[random.uniform(self.bounds[i][0], self.bounds[i][1]) for i in range(self.dim)] for j in range(self.pop_size)]
        # 初始化策略和参数
        strategy = [0.5] * self.dim
        scaling_factor = 0.5
        crossover_rate = 0.5

        for i in range(max_iter):
            for j in range(self.pop_size):
                # 根据策略和参数生成新的个体
                a, b, c = self.select_samples(pop, j)
                mutant = self.mutate(pop, a, b, c, scaling_factor, strategy)
                trial = self.crossover(pop[j], mutant, crossover_rate)

                # 计算适应度
                f_trial = self.f(trial)
                if f_trial < self.f(pop[j]):
                    pop[j] = trial

                    # 更新策略和参数
                    strategy, scaling_factor, crossover_rate = self.update_strategy_and_parameters(strategy, scaling_factor, crossover_rate, a, b, c, trial, pop)

        best = min(pop, key=self.f)
        return best

    def select_samples(self, pop, current_index):
        # 选择三个不同的个体
        a, b, c = random.sample(range(self.pop_size), 3)
        while a == current_index:
            a = random.randint(0, self.pop_size - 1)
        while b == current_index or b == a:
            b = random.randint(0, self.pop_size - 1)
        while c == current_index or c == a or c == b:
            c = random.randint(0, self.pop_size - 1)
        return a, b, c

    def mutate(self, pop, a, b, c, scaling_factor, strategy):
        # 根据策略和参数生成新的个体
        mutant = [pop[a][i] + scaling_factor * strategy[i] * (pop[b][i] - pop[c][i]) for i in range(self.dim)]
        return mutant

    def crossover(self, target, mutant, crossover_rate):
        # 生成新的个体
        trial = []
        for i in range(self.dim):
            if random.random() < crossover_rate:
                trial.append(mutant[i])
            else:
                trial.append(target[i])
        return trial

    def update_strategy_and_parameters(self, strategy, scaling_factor, crossover_rate, a, b, c, trial, pop):
        # 计算新的策略和参数
        new_strategy = []
        for i in range(self.dim):
            if random.random() < 0.1:
                new_strategy.append(random.uniform(0, 1))
            else:
                new_strategy.append(strategy[i])
        new_scaling_factor = scaling_factor * random.gauss(1, 0.1)
        new_crossover_rate = crossover_rate * random.gauss(1, 0.1)

        # 判断是否更新策略和参数
        if self.f(trial) < self.f(pop[a]) and new_scaling_factor > 0 and new_crossover_rate > 0:
            return new_strategy, new_scaling_factor, new_crossover_rate
        else:
            return strategy, scaling_factor, crossover_rate

使用方法：

def f(x):
    # 目标函数
    return x[0] ** 2 + x[1] ** 2

bounds = [(-5, 5), (-5, 5)]
sade = SADE(pop_size=10, f=f, cr=0.5, dim=2, bounds=bounds)
best = sade.optimize(max_iter=100)
print(best)

其中，`pop_size`是种群大小，`f`是目标函数，`cr`是交叉率，`dim`是维度，`bounds`是变量的取值范围。在`optimize`方法中，首先初始化种群和策略、参数，然后执行若干代的优化，每次选择三个不同的个体，根据策略和参数生成新的个体，进行交叉和变异，计算适应度，更新种群和策略、参数。最后返回最优个体。